Computational modeling of elastic constants as a function of temperature and composition in Zr–Nb alloys

The effect of crystallographic mismatch on the obstacle strength of second phase precipitate particles in dispersion strengthening: bcc Nb particles and nanometric Nb clusters embedded in hcp Zr

“…1d, there is no intermetallic compound in the ZreNb binary phase diagram [12]; the bcc structure is a sole equilibrium configuration possible for solute Nb atoms precipitating from the Zr matrix. The bcc Nb particles are softer than the hcp Zr matrix with respect to shear modulus (28 GPa [13,14] and 33 GPa [15], respectively); however, they may be the Orowan-type strong obstacles due to the following crystallographic constraint. Unlike the well-known crystal orientation relationship proposed by Burgers for taking into account the structural transition from bcc Zr to hcp Zr, {0002} hcp //{110} bcc [16,17] [10,11]; the discrepancy arises presumably because the lattice constant of bcc Nb is significantly different from that of bcc Zr: the difference is~8.5% [10,11].…”

Size- and temperature-dependent Young's modulus and size-dependent thermal expansion coefficient of thin films

“…[24][25][26] Atomistic simulations have also been conducted to study temperature-dependent elastic constants. [27][28][29][30] Combining firstprinciples calculations and phonon calculations, Wang et al computed elastic stiffness coefficients of Al, Cu, Ni, Mo, Ta, NiAl, and Ni 3 Al as a function of temperature for the temperature range from 0 K to each melting point of the crystals and the results agreed with the experimental measurements. 27 The temperature-and composition-dependent elastic constants of bcc Nb 1Àx Zr x (0 r x r 1) solid solution phase were analyzed with the thermodynamic CALPHAD-type model, where the values of critical parameters were determined from available experimental data and first-principles calculations.…”

Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials

“…Many materials properties, e.g., elastic constants, feature a qualitatively very different behavior if finite-temperature contributions are taken into account [13][14][15][16]. For example, thermal expansion typically causes a strong decrease of elastic constants.…”